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6-2016

Hope and Health Related Quality of Life of Older Women Who Hope and Health Related Quality of Life of Older Women Who Have Had Heart Attacks

Have Had Heart Attacks

Alice Mary Kelly-Tobin

Graduate Center, City University of New York

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HOPE AND HEALTH RELATED QUALITY OF LIFE OF OLDER WOMEN WHO HAVE HAD HEART ATTACKS

by

Alice Mary Kelly-Tobin

A dissertation submitted to the Graduate Faculty in Nursing in partial fulfillment of the Requirements for the degree of Doctor of Philosophy, The City University of New York

2016

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© 2016

Alice Mary Kelly-Tobin All Rights Reserved

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Approval Page

Hope and Health Related Quality of Life of Older Women who have had Heart Attacks by

Alice Mary Kelly-Tobin

This manuscript has been read and accepted for the Graduate Facility in Nursing in satisfaction of the dissertation requirement for the degree of Doctor of Philosophy

April 15, 2016 ____________________________________

Martha Whetsell, RN, PhD, FAAN

Date

Chair of Examining Committee

April 15, 2016______ ____________________________________

Donna Nickitas, RN, PhD Date

Executive Officer

Supervisory Committee:

Stephen Baumman, RN, PhD William T. Gallo, PhD Frances Lafauci, RN, EdD Miguel Villegas-Pantoja, RN, PhD

THE CITY UNIVERSITY OF NEW YORK

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Abstract

HOPE AND HEALTH RELATED QUALITY OF LIFE OF OLDER WOMEN WHO HAVE HAD HEART ATTACKS

by

Alice Mary Kelly-Tobin Advisor: Dr Martha Whetsell

Background: Heart disease is the number one cause of death and leading cause of disability in adults in the United States. Coronary heart disease (CHD) is the most common form of heart disease with heart attack as its acute manifestation. Health Related Quality of Life (HRQoL) is a multidimensional concept of self-perception of physical, emotional health, and overall sense of well-being. Hope, an inner process focusing on maintaining physical and mental well-being, is considered necessary for survival of chronic illnesses, such as CHD.

Method: Women age 65 and older who have had heart attacks (N=91) volunteered to participate in this quantitative non-experimental correlational study. They completed a demographic

questionnaire, SF12 and HHI.

Findings: Marital status was related to HHI scores, t(90) = -2.70, p = .041, with married participants having greater mean score (40.87) compared to singles (38.39). General Health, r(89) = .244, p = .02; Mental Health, r(89) = .352, p = .001; Vitality, r(89) = .221, p = .035;

Social Functioning, r(89) = .333, p = .001 and Role Emotion , r(89) = .223, p = .034 correlated with HHI. Marital Status , t(89) = 2.07, p = .041 and Mental Health, t(88) = 3.40, p = .001, best predicted the HHI and explained an adjusted total of 13.8% of variance in HHI scores, R = .397, F(1,88) = 8.21, p = .001. Relationship between HRQoL and HHI revealed significant findings pertaining to age and ethnicity. Cronbach's alpha on HHI (α=.838) and SF 12 (α=.822) revealed

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internal consistency.

Keywords: Older women, heart attack, hope, health related quality of life

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Acknowledgements

Over the past six years, I have received support and guidance from many individuals. Dr Martha Whetsell has been a mentor, colleague and friend. I will never forget what she said to me and members of my cohort on our first day in her class, “You are my students and I honor you.”

Dr Whetsell’s kind facilitation, inspiration and encouragement was more helpful to me than she could ever know. I am most grateful to have had the opportunity to work with Dr Whetsell.

I would like to thank my dissertation committee of Dr Steven Baumann, Dr William Gallo, Dr Fran LaFauci and Dr John Jerome for being generous with their time and support as I moved from proposal to completed study. Their input was insightful and valuable. Dr La Fauci has been one of my mentors and she inspired me to obtain this doctoral degree.

I am grateful to the faculty of the Nursing Doctoral Program at CUNY Graduate Center.

Dr Donna Nickitas provided strong leadership and she contributed her support for my study.

Dr Eileen Gigliotti’s unique and effective way of teaching Statistics was beneficial. Dr Keville Frederickson offered encouragement and support that helped me start this process. Dr Marge

Lunney was frequently “on my shoulder” as I wrote and re-wrote the chapters of my dissertation.

Dr Violet Malinsky guided my understanding and application of theory and conceptual frameworks. Dr Alicia Georges widened my worldview and expanded my understanding of health disparities. Dr Kathleen Nokes introduced me to large datasets and that is where I found HRQoL which is a foundation of my study.

Special thanks go to my cherished colleague, Dr Miguel Villegas. He kindly devoted time to review my work and his focused advice was invaluable. I am appreciative of the support and proofreading assistance provided by my wonderful colleague and friend, Dee Fabian. My

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friend and mentor, Dr Kathleen Burger provided me with excellent pointers throughout this endeavor.

The Suffolk County Department of Health and the Office of the Aging were most helpful in assisting me in gaining entry to the county senior centers where I recruited my study sample. I am very thankful to the wonderful women who volunteered to participate in my study.

Many were so enthusiastic and willing to help.

Joy Borrero and Margarett Alexandre are colleagues, classmates and friends. We started this journey together and we have provided each other with support and encouragement along the way. This journey would have been lonely and much more difficult without them. My Suffolk County Community College colleagues have been steadfast supporters as they offered frequent encouragement. My students also have followed me with interest during my doctoral journey which invigorated me and my efforts.

I must thank Starbucks for providing me a safe and caffeine charged environment in which I accomplished much writing.

Finally I thank my parents. My Dad died on my first day of classes in the doctoral program. He was so pleased and proud that I was going to pursue a PhD. My strongest, most dedicated supporter, champion and advocate is my beloved spouse, Michael. He endured my spectrum of moods with love and unwavering cheerful acceptance. He is the best partner I could ever hope for. He has helped me more than I could express and I am most grateful.

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Dedication

This is dedicated to the one I love, my amazing husband, Michael. His loving support and encouragement sees me through everything I do. My doctorate is his doctorate too because I could not have done this without him. I love you more every day.

Everything that is done in the world is done by hope. Martin Luther King, Jr.

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Hope and Health Related Quality of Life of Older Women who have had Heart Attacks Table of Contents

ABSTRACT iv

ACKNOWLEDGEMENTS vi

DEDICATION viii

CHAPTER 1: RESEARCH OBJECTIVE 1

A. Background B. Problem Statement

C. Research Question Need for the Study D. Purpose of the Study

E. Significance

F. Definitions of Terms G. Theoretical Framework H. Delimitations

I. Limitations

J. Organization of Study and Chapter Summary

CHAPTER 2: REVIEW OF LITERATURE 13

A. Introduction B. Literature Review

C. Organization of Study and Chapter Summary

CHAPTER 3: METHODS 25

A. Introduction B. Sample Population

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C. Instruments

D. Data Collection Procedures E. Protection of Human Participants F. Data Analysis

G. Chapter Summary

CHAPTER 4: RESULTS 31

A. Results

B. Statistical Analysis

1. Descriptive Statistics: Sample Characteristics 2. Health Related Quality of Life

3. Demographic Variables and HRQoL and Herth Hope Index 4. Multiple Regression Analysis

5. Additional Analysis C. Summary of Results D. Chapter Summary

CHAPTER 5: FINDINGS 51

A. Discussion

B. Overview of study C. Theoretical Rationale

D. Study Sample Characteristics E. Interpretation of Findings F. Strengths and Limitations G. Chapter Summary

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CHAPTER 6: CONCLUSIONS 57 A. Implications for Nursing and Healthcare Practices

B. Recommendations for Future Research

C. Conclusions

APPENDICES 63

Appendix A HRQoL SF12 Appendix B Herth Hope Index

Appendix C Demographic Questionnaire

REFERENCES 70

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Hope and Health Related Quality of Life of Older Women who have had Heart Attacks List of Tables and Figures

Table 1 Distribution of Age 32

Table 2 Distribution of Concurrent Medical Illness 33

Table 3 Distribution of Ethnicity 34

Table 4 Distribution of Marital Status 34

Table 5 Distribution of Education Level 35

Table 6 T test Comparison between Sample Means and National Means 36 Table 7 T test Comparison between SF12 and HHI based upon Age 37 Table 8 T test Comparison between SF12 and HHI based upon Ethnicity 39 Table 9 T test Comparison between SF12 and HHI based upon Marital Status 40 Table 10 T test Comparison between SF12 and HHI based upon Education 42

Table 11 Pearson Correlation of SF12 and HHI 43

Table 12 Frequency Distribution of number of Concurrent Illnesses 44 Table 13 Multiple Regression Predicting HHI scores based upon SF12 Responses 46 Figure 1.1 Wilson and Cleary’s Health Related Quality of Life Conceptual Model 9

Figure 3.1 Domain Descriptions 27

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Chapter One Research Objective Background

Heart disease is the number one cause of death in the Unites States and it is responsible for approximately 600,000 deaths annually (Santulli, 2013). Heart disease is also a leading cause of morbidity and disability in adults (Centers for Disease Control and Prevention [CDC], 2013).

Coronary heart disease (CHD), the most common form of heart disease, causes more than 385,000 deaths or 1 in 5 deaths in each males and females, annually (CDC, 2013). CHD is a chronic condition in which atherosclerotic plaque builds up in coronary arteries and causes blockage of blood flow to cardiac muscle tissue. The blockage of blood flow to cardiac muscle and the resultant tissue death is called myocardial infarction, also known as heart attack (NIH, 2013). Each year over 700,000 heart attacks occur in the United States and over 500,000 are first time heart attacks (NIH, 2013).

Older adults have a disproportionately higher prevalence of CHD and heart attacks than any other age group (CDC, 2012a). Older adults with CHD and heart attack have varied health needs and they demonstrate different intervention outcomes, hence perceptions of their physical and mental well-being vary widely. Quality of life, leading health indicators and measures of well-being are tracked as Health Related Quality of Life (HRQoL) in the United States by the Centers for Disease Control and Prevention (CDC, 2012b).

HRQoL is the self- perception of an individual’s level of functional, well-being and overall evaluation of general health (Stewart & Ware, 1992). The CDC defines HRQoL as a multidimensional concept of self-perception of physical, emotional health, and the effect upon sense of well-being. Older adults report lower HRQoL scores than other age groups (CDC,

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2012b). Tools that measure HRQoL focus upon physical symptoms, functional ability and mood, however, internal operations that influence perceptions and support physical and mental health such as hope, are not included in tools that measure HRQoL.

Hope has been identified as a significant emotional and intellectual attribute of

individuals with chronic illnesses and it has been described as essential to life (Duggleby et al., 2012). Hope is defined by one nurse researcher as an inner process focusing on maintaining physical and mental well-being (Herth, 1993). Hope has many dimensions that change in different situations and hope has been identified as a positive attribute in chronic illness (Wiles, Cott, & Gibson, 2008; Duggleby, et al., 2012). Hope allows individuals to have positive

expectations of their present and future circumstances (Herth, 2007). Attributes of hope include realistic understanding of circumstances, ability to consider alternatives and ability to set goals (Morse & Doberneck, 1995).

This study focuses upon the relationship between hope and HRQoL of community dwelling women, age 65 years and older, who have had heart attacks. The incidence and prevalence of heart attacks is greatest among women, age 65 and older, as approximately

200,000 women die from heart attacks in the US annually (CDC, 2013). However, there are few research studies that focus upon women who have had heart attacks and their consequent

perceptions on the quality of their lives or their perceived levels of hope.

In order to understand the health related quality of life of women, age 65 years and older, who have had heart attacks, it is necessary to understand basic information about quality of life (QoL), surveillance of HRQoL, basic information about older women and the prevalence of chronic coronary heart disease and heart attacks. QoL is defined as a state of complete physical, mental and social well- being (World Health Organization [WHO], 2012). HRQoL is an aspect

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of QoL that focuses on perceptions of health. HRQoL is measured and monitored internationally by the WHO (2012) and nationally by CDC (2012b).

HRQoL data is analyzed by Behavioral Risk Factor Surveillance System (BRFSS) and National Health and Nutrition Examination Survey (NHANES), in order to identify poor perceived health in specific groups or trends in specific regions. HRQoL data provides insight into the degree of disruption to physical and mental health and daily activities. This data enables nurses and other health care providers to predict outcomes of treatment, mortality and morbidity and to prevent functional limitations and disabilities (CDC, 2012b). HRQoL data informs public health policies and legislation in order to develop public health and legal actions and to monitor the effectiveness of the actions (CDC, 2012b). In fact, improving the HRQoL scores has been one of the goals of Healthy People 2000, 2010 and 2020.

Adults over age 65 years report fair to poor HRQoL scores more frequently than other age groups, according to BRFSS (CDC, 2012b). Women in all age groups were more likely to report fair to poor HRQoL scores than men (CDC, 2012b). Older adults suffered the poorest physical health and the most activity limitation (CDC, 2012b). There are 35 million adults, age 65 years and older, in the United States and women account for over 20 million (Howden &

Meyer, May, 2011). Women live an average of six years longer than their male counterparts (Fowles & Greenberg, 2011). The life expectancy of women in 2012 was over 80 years;

however, challenges of longevity frequently include disability related to chronic illness or inability to live alone in last years of life (Fowles & Greenberg, 2011). Approximately 45% of women over age 65 are widowed and they tend to be widowed for 15 to 20 years (Administration on Aging, [AOA], 2012). Many women, age 65 years and older, are divorced or were never married (Howden & Meyer, 2011). Nearly 80% of all older adults living alone are women

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(Fowles & Greenberg, 2011). Additionally older minority women are more likely to be poor or near poor with annual incomes of less than $15,000 (Howden & Meyer, 2011). In summary, when compared to their male counterparts, women live longer; they are widowed earlier and they tend to live alone. They are often impoverished and they experience more years of chronic illnesses and disabilities.

CHD is one of the most prevalent chronic illnesses among adults, age 65 years and older, at 19.8% (CDC, 2011). Approximately 8 million women are diagnosed with heart disease annually (CDC, 2013). Over 700.000 men and women have heart attacks annually. More women than men die of heart attack; approximately 200,000 women die annually (American College of Cardiology, 2013). In fact 23% of women die within one year of a first heart attack and up to 32% who survive die within 5 years of a first heart attack (American College of Cardiology, 2013). Impairments and disabilities related to persistent symptoms of CHD and heart attack include decreased levels of physical and mental health, limitations in function and pain (CDC, 2011).

Recovery from a heart attack requires a positive attitude to make the lifestyle changes that promote stability of CHD (Boehm & Kubzansky, 2012). A comprehensive review by Boehm & Kubzansky (2012) focused on research studies that examined the effect of positive psychological well-being on health behaviors and biological attributes on cardiovascular health.

They cited hope as an aspect of positive psychological well-being. Their findings suggest a link between positive attitude and the reduction in cardiovascular events with increased restorative health behaviors. Therefore, hope may have an indirect relationship with positive outcomes in CHD.

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Problem Statement

Data shows many women, age 65 and older, report fair to poor HRQoL scores (CDC, 2012b). CHD is the leading cause of death in the US, results in an annual death rate of 200,000 women annually, according to research. However, there is a gap in research studies from 1990’s to 2014 that focus upon HRQoL or hope of women over age 65. The relationship between hope and HRQoL of older women who have had heart attacks has not been explored.

Research Question

1. Is there a relationship between hope and health related quality of life of older women, who have had heart attacks?

Purpose of the Study

The purpose of this research was the examination of hope and the HRQoL of older women who have had heart attacks in order to describe the relationship between hope and health related quality of life of this population. Previous studies of HRQoL have focused upon adults of all ages and gender with specific diseases such as heart disease, cancer, arthritis and diabetes mellitus type 2 (Khanna et al., 2011, White, Wheelwright, Fitzsimmons, & Johnson, 2012;

Williams et al., 2012) or adults living in long term care facilities (Kanwar et al., August. 2013;

Van Malderen, Mets, & Gorus, 2013). There is a gap in research studies from 1990’s and 2104 that focuses upon women, heart attacks and the relationship between hope and HRQoL.

Significance of Study

This study is significant because it focused on older women who have had heart attacks and it examined the relationship between HRQoL, as multidimensional assessment of health perceptions and hope, as an inner process related to physical and mental health. The population of women over age 65 is growing and they are a vulnerable group because they are likely to live

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alone and in poverty. Healthy People 2020 and the Institute of Medicine have identified HRQoL as a focus of the US health agenda (Healthy People, 2013).

The HRQoL of older women who have had heart attacks reveals their most troubling problems and disruptions to their day to day function (CDC, 2012b). HRQoL is an indicator of unmet health needs. Increased assessments of HRQoL in older women who have had heart attacks provide nurses and other members of the health care team with information concerning priority problems such as functional status, pain and activity limitations.

Older women who have had heart attacks are faced with lifestyle changes as the result of the symptoms associated with CHD. Lifestyle changes influence stability of their disease.

Studies suggest a relationship between positive psychological well-being and physical health (Pressman & Cohen, 2005; Diener & Chan, 2011; Duggleby, et al., 2012). Moreover, links between positive psychological well-being and coronary heart disease have also been noted (Boehm, Peterson, Kivimaki & Kubzansky 2011a; Boehm, Peterson, Kivimaki, Kubzansky, 2011b). Consequently, an indirect link between hope and positive CHD outcomes has been suggested by a comprehensive review of such studies. A better understanding of hope in older women who have had heart attacks assist nurses and other health care providers to foster hope and positive psychological well-being in order to promote positive CHD outcomes.

Health perceptions and levels of hope are aspects of health that contribute to effective treatment plans and better outcomes in CHD. HRQoL enables nurses and other health care providers to focus on the most troubling symptoms, functional level and unmet needs.

Measurement of hope assists clinicians in applying aspects of positive psychology, hope and goal setting to treatment plans. Discovering a relationship between HRQoL and hope reveals

additional links to positive outcomes in older women who have had heart attacks.

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Definitions of Terms

Functional abilities: degree to which individuals can independently perform activities of daily living (ADL) and instrumental activities of daily living (IADL).

Health Related Quality of Life (HRQoL): The self- perception of an individual’s level of physical health mental well-being, functional abilities and overall evaluation of general health (Stewart & Ware, 1992). HRQoL was measured with the HRQoL SF 12.

Heart Attack: The blockage of blood flow to cardiac muscle and the resultant tissue death, also called myocardial infarction (NIH, 2013).

Herth Hope Index (HHI): Questionnaire that measures hope. The questions probe inner thoughts and feelings about outlook on life, goals and fears. Scores range from 12 to 48, with 12 as the lowest and 48 as the highest. Lower scores represent less hope and higher scores represent more hope.

Hope: An inner process focusing on inner thoughts and feelings about outlook on life, goals and fears in order to maintain physical and mental well-being (Herth, 1999). Hope was measured by the Herth Hope Index

HRQoL SF 12: Questionnaire that measures HRQoL. It is one of the most widely used tools to measure health status from the perspective of the individual participants. Scores range from 0 to 100, with 0 as lowest score and 100 as the highest. Lower scores indicate lower HRQoL and higher scores indicate higher HRQ0L.

Theoretical Framework

Several theoretical frames (Ferrans, Zerwic, Wilbur & Larson, 2005; Sprangers &

Schwartz,1999; Zubritsky, Abbott, Hirschman, Bowles, Faust & Naylor, 2012.) pertaining to HRQoL exist, however, the Wilson and Cleary model supports this study and it is strongly

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supported by repeated empirical testing (Costa & King, 2013). Wilson and Cleary (1995) devised a conceptual model of HRQoL that linked patient health, medical outcomes and clinical variables with quality of life. The conceptual model describes links between the five domains:

biological/physiological, symptoms, functional status, general health perceptions and overall quality of life. Characteristics of the individual and the environment are also linked to HRQoL in Wilson and Cleary’s conceptual model. Wilson and Cleary (1995) posit that illness or disease disrupt biological/physiological functions which causes symptoms. Some symptoms disrupt function which influences general health perception and finally effects overall HRQoL.

Biological/physiological domain encompasses cellular, organ and organ systems functions such as height/weight, serum values, physiologic measures, medical history and medications. A symptom is the perception of physiological, psychological or cognitive

abnormality as it relates to the biological/physiological domain, according to Wilson and Cleary (1995). Functional status refers to level of ability to carry out physical, social, role and

psychological tasks as influenced by biological/physiological status and symptoms. General health perception is based upon the integration of biological/physiological domain, symptoms and functional status. Health related quality of life is the individual’s perception of their quality of life with relation to aspects of their physical and mental health (Zubritsky, et al., 2012)

Characteristics of the individual such as personality, values, motivation and preferences influence the response to the level of disruption that illness and symptoms cause and ultimately to the overall HRQoL (Nokes, et al., 2000; Wiles, Cott & Gibson, 2008; Ferguson, 2013).

Additional individual characteristics that influence the response to illness include age, gender, education, race, ethnicity, marital status and cognitive status (Wilson & Cleary, 1995; Naylor, 2004; Ferguson, 2013).

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Characteristics of the environment such as psychological support and social and

economic supports also influence the overall HRQoL. Additional environmental characteristics include structural features of the home environment, support system provided by family, friends and community, accessibility to services (such as grocery for nutritious food, pharmacy for medications, hospital and health care providers), transportation, relationship with health providers and health delivery system (Beck, 2014).

Figure 1.1. Wilson and Cleary’s Health-related quality of life conceptual model. Reproduced with permission from Wilson I.B., & Cleary, P.D. (1995). JAMA, 273, 59-65. Copyright © (1995) American Medical Association.

All rights reserved. (See Permission Appendix L).

Nursing theorists updated and clarified some aspects of Wilson & Cleary’s conceptual framework in 2005 (Ferrans, Zerwic, Wilbur & Larson, 2005). Ferrans and her colleagues posited that individual characteristics, lifestyle and environmental factors may have a stronger

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influence than biological factors. For example, individuals who are sedentary or who consume high fat foods or those who live in areas with high levels of air pollution may account for higher risk for illness than hereditary or biological factors. Non-medical factors were removed from the model, as such attributes are included in the characteristics of the individual and the environment in the concept revised by Ferrans and her colleagues. The treatment of the Wilson & Cleary model by Ferrans and her colleagues has resulted in greater clarity and it facilitates more general application of the conceptual model.

Aging and chronic illness cause changes in the health domains of HRQoL:

biological/physiological, symptoms, functional status, general health perceptions and overall quality of life. Older adults experience normal changes associated with aging such as loss of lean muscle mass, decreased muscle strength, diminishing near or close up vision and loss of skin elasticity (Fulop et al., 2010). In addition, many experience changes caused by chronic illnesses such as gait disturbances, low energy and sensory disturbances (Weiss, 2011). Women, age 65 and older, who have had a heart attack are likely to experience symptoms caused by CHD such as fatigue, chest pressure or pain and heart failure (Carney & Freedland, 2012).

Women with symptoms caused by CHD and changes associated with aging and other chronic illnesses also report decreased functional abilities and increased periods of depression and hopelessness (Carney & Freedland, 2012). Studies have shown that hopelessness is linked to functional decline (Muramatsu, Yin, & Hedeker, 2010; Huang, Dong, Lu, Yue, & Liu, 2010;

Hirsch, Sirois, & Lyness, 2011). HRQoL data reveals a correlation between functional decline to quality of life. Greater functional decline results in lower quality of life and HRQoL. However, there are studies that suggest that individuals with positive attitudes and positive emotional affects such as happiness, optimism and hope, experience less functional decline and less

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depression related to chronic illnesses (Wiles, Cott, & Gibson, 2008; Hirsch, Sirois & Lyness, 2011; Duggleby et al., 2012). Hope and higher HRQoL are linked to less functional decline and less depression in women with CHD.

Assumptions

(a) Each subject will respond truthfully; (b) their responses will reflect their perceptions of health related quality of life and hope; (c) most people do not think about health related quality of life; (d) hope is important to most people. Homogenous sample in the following areas:

socioeconomic levels, access to health and geographic location.

Delimitations

For ethical, methodological and theoretical reasons this study has only included adult women, age 65 years and older, who are fluent in English and are free of the diagnosis or

treatment of dementia. Therefore, the following were excluded: individuals who are not fluent in English and individuals who have been diagnosed with dementia. Translation of the HRQoL SF 12 questionnaire from English to some other languages may adversely affect the validity of the tool. Some studies suggest that proxy responses on HRQoL SF 12 may not accurately reflect the self-perception of health of older adults (Andresen, Vahle & Lollar, 2001; Sitoh et al., 2003;

McPhail, Beller, & Haines, 2008; Gabbe, et al, 2015).

Limitations

Homogeneity of the sample was initially assumed to be a limitation, however, variability of the sample was noted. Variability may also be considered a limitation.

Organization of the Study and Chapter Summary

This research study has been organized into six chapters. Chapter One introduced the study and it included the statement of the problem, need for the study, purpose of the study,

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significance of the study, definition of terms, theoretical framework, research questions, assumptions, delimitations and limitations of the study.

Chapter 2 presents the review of literature on HRQoL and hope related to older women. Chapter 3 outlines research methods. This includes selection of survey sample, tools to be used, data to be collected and data analysis procedure. Chapter 4 presents study results, which includes demographic information and results of data analysis. Chapter 5 presents summary of the study and interpretations of findings. Chapter 6 presents implications for practice, recommendations for future research and final conclusions.

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Chapter 2

This chapter presents the review of literature pertaining to HRQoL and hope in older adults. Literature about HRQoL offers an overview, general trends and survey data specific to older adults and current HRQoL research. Literature concerning hope provides the definition, the role of hope and research findings concerning hope in older adults. The summary adds general demographic trends pertaining to women, age 65 and older, and pertinent US census data. Finally, the summary reviews similarities and differences in research findings and how they pertain to the relationship between hope and health related quality of life of women age 65 years and older.

Review of Literature

Health Related Quality of Life

Quality of life is defined as a holistic phenomenon of complete physical, mental and social well- being (Skevington, Lotfy, & O’Connell, 2004). HRQoL is an aspect of overall quality of life. HRQoL is the self- perception of an individual’s level of functional, well-being and overall evaluation of general health (Stewart & Ware, 1992). HRQoL is defined as the multidimensional concept of self-perception of physical and emotional health and the effect upon sense of well-being (CDC, 2012b). HRQoL provides information concerning health needs and areas in which additional supports are needed to optimize health and promote wellness. HRQoL is measured and monitored internationally by the WHO (Skevington et al., 2004; Visser, et al., 2014). nationally by the CDC (2012b).

It is the mission of the CDC to provide HRQoL data for analysis with and interpretation by public surveys such as the National Center for Health Statistics (NCHS), National Health and Nutrition Examination Survey (NHANES), Behavioral Risk Factors Surveillance Surveys

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(BRFSS) and Healthy People 2020 in order to identify poor perceived health in specific groups or trends in specific regions (CDC, 2012b). HRQoL survey results are considered indicators of health needs and outcomes, as well as predictors of mortality and morbidity (CDC, 2012b).

Tracking of HRQoL results have exposed trends in health problems in adults of all ages.

Most HRQoL research rely upon the SF 36 or SF 12 tool. Trends reveal that chronic illness, age, gender, socioeconomic status, ethnicity and level of education negatively impact HRQoL

(Moriarty, Kobau, Zack, & Zahran, 2005; Visser, et al., 2014). The data reveals that adults, age 65 and older, report the lowest overall HRQoL scores, most physically unhealthy days per month, poorest physical health, high prevalence of pain and sleeplessness and most activity limitation of all respondents from 2000 to 2010 (CDC , 2012b).

HRQoL data as interpreted by BRFSS in 2003 revealed marked differences related to socioeconomics, gender and level of education (Moriarty et al., 2005). Men and women over age 65 years with college degrees and annual incomes of $50,000 or more per year, reported an average of 3.5 and 4.0 unhealthy days per month, respectively. Conversely, men and women over age 65 years without high school diplomas and annual incomes of $15,000 /year or less reported lower health related quality of life and 8.8 and 10.1 unhealthy days, respectively each month on average. Specifically, women, age 65 years and older, with lower levels of education and low income reported the lowest health related quality of life due to physical illness, injury, stress, pain, sleeplessness, depression or emotional problems one out of three days in the preceding month.

Gender differences in health related quality of life were noted in a cross-sectional analysis of findings (n = 50,573) by 2004 BRFSS (Ford et al., 2008). The study investigated unhealthy days related to physical symptoms, mental symptoms and activity limitations in men

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and women with coronary heart disease (CHD) as compared to those without CHD. The study revealed statistically significant differences in people with CHD and between men and women.

In general, women reported experiencing the most unhealthy days and activity limitation. In addition, men and women with CHD reported approximately two times the number of physically unhealthy days and activity limitation days compared to those without CHD. Women with CHD reported more frequent physically and mentally unhealthy days and days of activity limitation when compared with men. The researchers concluded that individuals with CHD experience significantly lower health related quality of life.

Comprehensive comparative analysis of data collected in four national surveys from 1971 to 2007 (collective n > 4,000 to 400,000 per study from 1993 to 2007) revealed gender

differences in self-rated general health (Salomon, Nordhagen, Shefali, & Murray, 2009). Their analysis of data collected by BRFSS, Current Population Survey (CPS), National Health and Nutrition Examination Survey (NHANES) and National Health Interview Survey (NHIS) suggest health related quality of life is impacted by marital status, low income and level of education. The findings become even more significant when the following 2012 US Census data are considered: 45% of women over age 65 years are single (never married, widowed or

divorced) and the median household income of such women is just over $15,000/year which is considered poor or near-poor (AOA, 2012).

The large data set (n = 88,062) of HRQoL responses collected in 2000-2001was analyzed by BRFSS for differences based upon ethnicity (Chowdhury, Baluz, & Strine, 2008). The study focused upon general health, frequency of physical and mental distress and frequency of activity limitation. In general, Hispanics (n = 12,336), American Indians/Alaska Natives (n = 2,931) and Blacks (n = 14,937) were more likely to report fair to poor general health compared to Whites (n

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= 53,290) at 12.6% and Non-Hispanic Asians (n = 3,842) at 8.5%. American Indians/Alaska Natives reported the most frequent physical distress (18%) and mental distress (15.6%) compared to Asians (5%). Lower mean scores, or poor perceived health reported found in minorities, and those with low levels of education and lower income. Additionally, those with chronic diseases, such as arthritis and cardiovascular disease reported lowest HRQoL scores.

(Chowdhury, Baluz, & Strine, 2008).

HRQoL and health behaviors of men and women with ischemic heart disease (IHD) were analyzed in a national population based study in Denmark. Data was collected via personal interviews and self-administered questionnaires of adults, age 35 and older (n = 10,983), and then the sample was divided into three mutually exclusive groups: men and women with IHD, other chronic illnesses and no chronic illnesses (Alphin, Kjoller, Davidsen, Nissen, & Zwisler, 2012). The results were presented with 95% CI and the findings revealed the IHD group reported the lowest health related quality of life mean scores in the physical and mental components. Those with IHD were more likely to be older than the other groups. They were more likely to be obese (26%), sedentary (21%); and to smoke (40%). Additionally, the IHD group was more likely to take medications and utilize the health care system (87.8%). The IHD group also reported the most comorbid diseases such as musculoskeletal disease (23.6%), endocrine/metabolic (14.0%), respiratory disease (10.4%) and nervous system disease (5.0%).

The researchers concluded that men and women with ischemic heart disease (IHD) have more difficulty in their daily lives than those in the other groups. The findings suggest that such individuals are likely to experience improvement in health and health related quality of life through participation in wellness promotion and education programs that prevent or modify risks of smoking, obesity, imbalanced nutrition and sedentary lifestyle.

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A study of women with CHD revealed that educational interventions, cardiac rehabilitation program and increased physical activity were related statistically significantly higher HRQoL scores (Christian, Cheema, Smith, & Mosca, 2007). An ethnically diverse sample of women (n = 160) who were hospitalized for coronary heart disease were asked to volunteer and complete health related quality of life tool SF 36 at the time of admission and 6 months later. Paired t-test and multiple linear regression models were used to identify changes in health related quality of life and predictors of improvement. Findings (p=.005) suggest a

positive association between those who were married, employed, physically active, not depressed and enrolled in cardiac rehabilitation programs, as improved scores were noted in 6 months.

Conversely, women who were not married, not physically active, unemployed and depressed reported unchanged or lower HRQoL and may be at risk for poor clinical outcomes. Women at most risk reported feeling “down, depressed or hopeless.”

Hope

Hope has been identified as a basic and lifelong human reaction that stimulates successful coping and adaptation to life changes (Herth & Cutcliffe, 2002; Kornadt, Voss & Rothermund, 2015). Hope has been linked to wellness and illness experiences and recovery (Tutton, Seers, &

Langstaff, 2009; Duggleby, et al.,2012). It is thought to be necessary for survival of people with chronic diseases (Tutton, Seers, & Langstaff, 2009). Hope is a phenomenon that is experienced by people of all backgrounds. Studies suggest that higher levels of hope are related to better outcomes in academics, athletic competitions and health (Bailey & Snyder, 2007). Hope has been studied by various disciplines such as social sciences, psychiatry, theology and nursing.

Hope is a central concept of positive psychology (Bryant & Cvengeros, 2004; Boehm &

Kubzansky, 2012). A comprehensive review of positive psychological well-being research that

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examined the effect of positive psychological well-being on health behaviors and biological attributes of adults with heart disease (Boehm & Kubzansky, 2012). They noted studies that found relationships between positive psychology and physical health (Pressman & Cohen, 2005;

Diener & Chan, 2011). In addition, they noted research that linked positive psychology and CHD (Boehm, Peterson, Kivimaki & Kubzansky 2011a; Boehm, Peterson, Kivimaki, Kubzansky, 2011b). The studies suggest positive psychological well-being, which includes optimism and hope, is related to improved physical health and positive CHD outcomes (Boehm

& Kubzansky, 2012).

Hope is “to recognize the limitations of the situations while believing that opportunities exist,” according to Parse’s human becoming perspective (Parse, 1999b, p 3). Phenomenological studies conducted by Parse and a group of international nurse researchers across five countries and the United States revealed similarities and differences in the lived experience of hope (Baumann, 2004). These qualitative studies revealed group specific syntheses of hope along with some common themes. Baumann described a paradox of hope- no hope. He described the paradox as “restriction to freedom, tumultuous to peace, disheartening to inspiring and hope to no hope (Baumann, 2004, p. 343). Hope is a positive outlook in times of adversity or challenge, even though there is no reason for hope. It is linked to health healing and staying alive. Hope is also linked to religious faith, prayer and belief in afterlife. Findings reveal that hope is based on career or life accomplishments, volunteering and ability to contribute to one’s family and to society.

A concept analysis, using the Walker and Avant method, explored the construct of hope in older adults with heart failure (Caboral, Evangelista, & Whetsell, 2012). The study revealed that those with hope, trust in positive results without guarantees. Older adults with chronic

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diseases such as heart failure, experience pain, worry and sorrow. The concept analysis of hope suggests that nurses sustain hope by assisting older adults with heart failure to apply previous coping skills and by collaborating on setting realistic therapy goals.

One literature review explored hope as an emerging concept for nursing (Tutton et al., 2009). A strong link between hope and health was noted. Findings suggest that hope is based upon personal beliefs and adaptations to changes in family, health and environment that leads individuals to form a new and positive outlook or expectation of the future. Hope is linked to attaining goals and positive health outcomes. Setting realistic goals require cognitive process and energy focused upon attaining goals. Hopelessness, despair and loss of control causes vulnerable people to be at higher risk for poor health outcomes. Nursing interventions have a direct impact on patient outcomes and the literature suggests that nurses should learn about ways to foster hope when planning care and setting goals with their patients.

A considerable number of journal articles and research studies have examined the role of hope with mental illness or with terminal illness and critical illness such as cancer, HIV and trauma (Barker & Cutcliffe, 2000; Bassett, Lloyd, & Tse, 2008; Benzein, Norberg, & Saveman, 2001; Chi, 2007; Harris & Larsen, 2008, Herth, 1989; Johnson, Roberts, & Cheffer, 1996; Lohne

& Severinsson, 2006; Walker, 2004; Yeager et al., 2010). Fewer articles study hope with aging and chronic illness such as chronic pulmonary disease and post-stroke (Milne, Moyle, & Cooke, 2009; Tutton et al., 2012). Research revealed that levels of hope were positively related to physical health, mental health, psychosocial support and control of life. Those with impaired health maintained hope when relying upon other variables such as solid psychosocial support or ability to control their lives and futures. Younger to middle aged adults viewed hope in terms of maintaining control of their lives, planning their future life and reaching future objectives.

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Adults, age 65 and older, frequently experience health and life changes that limit their control and perception of the years to come (Herth & Cutcliffe, 2002). Hence, they may need to depend upon additional variables and strategies to foster hope. There are few studies that focus on hope in healthy aging adults who maintain good health and how such older adults preserve their positive outlooks while they experience changes associated with aging and declining health.

Nurses and older adults benefit from continuing research on hope, strategies to promote and sustain hope and methods to evaluate the effectiveness of such strategies.

Beckie, Beckstead and Webb (2001) created a conceptual model of women’s quality of life after cardiac events. They studied women (n = 93) after they had experienced a cardiac event such as heart attack and coronary artery bypass surgery and they tested the response of various QOL assessments to perceived health, hope and optimism. Perceived health was

measured by the SF-36, hope was measured by the HHI, and optimism was measured by the Life Orientation Test. QOL was measured by the Self-Anchoring Striving Scale, Faces Scale and Life 3 Scale. They found that hope and optimism had an influence on QOL (p = .382).

Herth and Cutcliffe (2002) reviewed three studies that focused on hope in older adults.

Each of the studies presented a 95% CI. The earliest, in 1979, examined ways of increasing hope in residents of a skilled nursing home (Mercer & Kane, 1979). The sample (n= 8) was asked to care for individually assigned plants and to participate in a resident council group. Comparison of pretests with posttests revealed decreased levels of hopelessness and increased physical activity. The researchers attributed the improvement in scores to increased control and purpose in the lives of the participants. However, the study is limited by the small sample size.

The second study (n= 239) tested the impact of interventions upon hope and quality of life in older adults (Staats, 1991). A series of five weekly training sessions were provided to four

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groups of older adults: one group received happiness training, the next group received goal setting classes, another group received a combination of happiness and goal setting classes and a final group was the control. No significant differences were noted among the groups in hope, but the results revealed that the combination group anticipated a higher quality of life 5 years into the future. The third research study (n= 50) concentrated on the effect of behavioral group therapy on older adults who had been widowed for 12 to 18 weeks (Herth, 1990). High levels of hope and effective grief resolution were noted in older adults who experienced no additional

significant personal loss and in those whose spouses died while in hospice care. Additionally, older adults who possessed positive self- directed coping skills evidenced high levels of hope and grief resolution.

Herth identified hope fostering strategies and hope hindrances in her study of the relationship between of hope to population characteristics of various home residences, such as private homes, senior housing apartments and long term care facilities (Herth, 1993). The sample of 60 older adults provided demographic information, completed the Herth Hope Index and they participated in semi structured interviews. Herth identified the following as categories of hope fostering strategies: interconnectedness, purposeful activities, uplifting memories, cognitive strategies, hope objects, refocused time, lightheartedness and spiritual/ philosophical beliefs and practices. Herth also found the following hope hindrances: hopelessness, depleted energy, uncontrollable pain/ suffering and impaired cognition. Levels of hope were not

significantly influenced by demographics, function or health. However, those living in long term care facilities reported lower levels of energy, more fatigue and less hope than older adults who lived at home.

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Chapter Summary

Review of literature revealed ample data and trends that point to variables that contribute to low self- perceived HRQoL scores. Low scores were linked to socioeconomic status, gender, education, marital status, ethnicity and chronic illnesses. In addition, data reveals that women, age 65 years and older, report the lowest HRQoL scores and the greatest number of unhealthy days, thus making them a vulnerable population.

Census report from 2000, paired with HRQoL data presented women, age 65 years and older, as a vulnerable population. The report revealed that men and women, over age 65 years, without high school diplomas and annual incomes of $15,000 or less reported lower HRQoL and more unhealthy days each month. Furthermore, it was noted that women were more likely to have low levels of education and low income and data shows that they consistently reported the lowest HRQoL due to physical illness, injury, stress, depression or emotional problems

(Moriarty et al., 2005).

Additionally, data shows that married older adults who reside with their spouses report higher HRQoL scores (Salomon et al., 2009). However, older adults with chronic illnesses, depression or those who were Black, Hispanic, American Indian and Alaskan reported the lowest HRQoL scores (Chowdhury et al., 2008). Census report from 2010, reveals that approximately 80% of women, age 65 years and older, are single and they live alone. Moreover, census reports reveal that women live six to seven years longer than men and they report more depression and chronic illnesses (AOA, 2012).

Research findings pertaining to hope reveal no links to population characteristics and few links to health or functional ability. Instead, low levels of hope are related to constant pain, low energy, impaired cognition, unresolved grief and undesirable living arrangements. Hope is

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associated to psychosocial relationships, connectedness ability to contribute and religious beliefs.

Hope is associated with positive outlook, ability to look toward the future and desire to set and achieve goals, according to the literature.

Review of research literature relevant to hope examines the effect of hope on younger to middle adults with chronic or severe illnesses. National and international studies of hope agree that hope is a positive frame of mind in times of adversity or challenge that is linked to health, healing and staying alive. Research suggests that hope is based on career or life

accomplishments, ability to contribute to society and religious beliefs (Baumann, 2004).

Another study revealed that older adults who resided in long term care facilities reported lower levels of hope than older adults living in community (Herth, 1993). Low hope was attributed to low energy, increased fatigue and lack of control of their lives. More research suggests hope in older adults with impaired health is sustained by relying upon psychosocial relationships, religious faith and ability to control their lives and futures (Herth & Cutcliffe, 2002; Piraino, Krema, Williams & Ferrari, 2014; Wu & Koo, 2016).

Some research studies suggest that hope has an impact on how adults cope with and adapt to their diagnosis and recommended lifestyle changes. Some studies suggest that high levels of hope and optimism have a positive effect on general health and may result in positive CHD outcomes. Census data and HRQoL trends indicate that poor HRQoL scores and a greater number of unhealthy days are anticipated for women, age 65 years and older, who have had a heart attack. Nurses are in a unique position to identify hope traits, hope sustaining strategies and hope hindrances to increase the level of hope in older adults with chronic illnesses.

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Chapter 3 presents research methodology of the study. This includes methods of:

research design, sample population, instruments, data collection procedure, protection of human participants and data analysis.

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CHAPTER 3 Methods

In Chapter 3 research methodology is presented. This includes research design, sample population, inclusion and exclusion criteria, along with the protection of human participants.

The instruments that were used are presented with information that highlights the validity and reliability of the tools. The type of data and data analysis procedures are also presented.

Research Design

This quantitative non-experimental correlational study analyzed the data in order to describe the relationship between hope and health related quality of life in older women who have had heart attacks. Data pertaining to hope and health related quality of life of the sample was measured through the use of the Herth Hope Index (HHI) and the SF 12. Sample size of 84 was calculated through the use of G* Power.

Prior to the full scale study, a pilot study was performed involving a sample of 10 volunteer participants who met the parameters for inclusion. The purpose of the pilot study was to test the tools and to assure the sampling frame and technique were effective.

Sample Population

The level of significance for the study was p = <.05 and power of .80 for medium effect.

G*power analysis (Erdfelder, Faul, & Buchner, 1996) determined the sample size (N > 84).

This convenience cluster sample was recruited from various senior groups including but not limited to senior nutrition centers, senior support groups and senior social groups.

Parameters for inclusion of voluntary participants were: female gender, age 65 years and older, history of a heart attack, living in community, ability to speak and read English without need of interpreter services, no diagnosis of cognitive impairment and ability to independently

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complete questionnaires with minimal prompting. The parameter of age 65 years and older is significant because the CDC HRQoL data is divided into age groups from 65 years to 85+

(CDC, 2012b). Using the same age parameters enabled comparison to national data. Further, 200,000 women, age 65 and older, have heart attacks annually (CDC, 2013). This accounts for approximately 80% of heart attacks in women annually.

Instruments

Hope was measured with the use of the Herth Hope Index (HHI). The HHI has been used by researchers in varied healthcare settings in diverse patient populations in the United States and internationally (Barker & Cutcliffe, 2000; Bassett, Lloyd, & Tse, 2008; Benzein, Norberg, &

Saveman, 2001; Chi, 2007; Harris & Larsen, 2008, Herth, 1989; Johnson, Roberts, & Cheffer, 1996; Lohne & Severinsson, 2006; Walker, 2004; Yeager et al., 2010). The HHI was designed by Dr Kaye Herth, a nurses, especially for nurses. HHI is easy to use and the results influence nursing practice. Dr. Kaye Herth, author of the Herth Hope Index has given her permission for use of her tool for this study.

The HHI is a 12 question tool that uses a 4 point Likert scale in which the higher scores represent the greater level of hope. The HHI questions probe inner thoughts and feelings about outlook on life, goals and fears. Scores range from 12 to 48, with 12 as the lowest score and 48 as the highest. One point is assigned to responses of “strongly disagree” and four points is assigned to responses of “strongly agree.” Lower scores represent less hope and higher scores represent more hope.

The HHI was assessed for validity by two committees of experts. The tool was evaluated for consistency with the conceptual definition of hope and it was reviewed for clarity and

readability of each question. Validity was established through measurement and comparison

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with other scales that measure hope. The HHI was determined as a valid instrument to measure hope (Herth, 1993). The HHI, as used in adults of all ages, was tested for reliability via test- retest and internal consistency and findings revealed Cronbach’s alpha scores of .91 and .97, respectively (Herth, 1993). Evaluation of questions and subscales revealed stability over time.

The internal consistency of the HHI was scored as Cronbach’s alpha score of .94, which is considered good validity.

HRQoL was measured with the use of the SF 12. The SF12 was chosen because it facilitates collection of data pertaining to the HRQoL health domains with the fewest questions.

HRQoL SF12 assesses eight domains: general health, physical functioning, role physical, pain, mental health, vitality, social functioning and role emotional. Descriptions of the domains are listed in table 3.1 (Kobau, Sniezek, Zack, Lucas & Burns, 2010). It is one of the most widely used tools to measure health status from the perspective of the individual participants.

Researchers have used SF12 in varied healthcare settings in diverse patient populations in the United States and internationally (Brazier & Roberts, 2004; Borglin et al., 2004; Gandhi et al., July, 2001; Larson, June, 2002; Montazeri et al., 2011; Resnick & Nahm, 2001; Sousa &

Williamson, 2003). Permission to use SF 12 was granted by Quality Metric.

Table 3.1

Domain Descriptions

______________________________________________________________________________

General Health Overall current health status

Physical Functioning Impact of health on usual physical activities Role Physical Impact of health on participation in daily activities

Pain Impact of pain on daily activities

Mental Health Control of behavior and psychological well-being

Vitality Impact of energy on daily activities

Social Functioning Impact of physical/mental health on usual social activities Role Emotional Impact of mental health on participation in daily activities ______________________________________________________________________________

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The SF 12 is a 12 question survey that utilizes a 5 point Likert scale in which higher scores represent a higher level health related quality of life (HRQoL). Scores range from 0 to 100, with 0 as the lowest score and 100 as the highest. Lower scores indicate lower health related quality of life and higher scores indicate higher health related quality of life (Ware, Kosinski, & Keller, 1996).

The SF 12, used on adults, was tested for reliability via norm-based scoring and test- retest method. Reliability was reported as Cronbach’s alpha of .89 for physical component scores. The internal consistency was reported as Cronbach’s alpha of .70 for physical

component scores. SF 12 is strongly correlated with the SF 36 and both are considered valid and reliable tools (Ware et al., 1996).

Data Collection Procedures

Female participants who were 65 years and older, from the various senior groups

described above gathered in a meeting room separate from the activities areas. A short education session on heart disease in women was presented first. Followed by a presentation of the

research study purpose, data collection procedure, data collection tools and questions, informed consent, confidentiality, voluntary participation guidelines and steps to immediately stop their voluntary participation. At the conclusion of the presentation, volunteers completed the

demographic questionnaire, HRQoL SF 12 and HHI surveys were administered by the researcher with the agreement and assistance of the senior center staff. As a gratuity for their willingness to listen to the presentation, all women over age 65 were provided with a gift valued under $5.

Estimated total time for the presentation, consent and completion of the questionnaire was approximately 40 minutes.

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Confidentiality was assured by excluding the names of participants and any identifiers from the questionnaires. None of the participants voiced concerns about their physical or mental health, but if they had, they would have been referred to their primary care providers or the Suffolk County Department of Health (SCDOH) clinics. The following Collaborative Institutional Training Institute (CITI) courses were taken to prepare the data collection

procedures: good clinical practice, information privacy and security, human subjects’ research and responsible conduct of research.

Protection of Human Subjects

It is vital to protect human subjects from harm or abuse as the result of research. The detailed proposal of the study was provided to the City University of New York Internal Review Board (CUNYIRB) through IDEATE. The following documentation were provided to the CUNYIRB: research proposal, methodology and tools to be used and authorizations to use the tools. The research plan included measures to assure confidentiality, informed consent and ways in which they can decline to participate or end their participation at any point during the study and maintenance of participant confidentiality. Biases, conflicts of interest or factors that could have influenced the outcome of research were not identified.

Data Analysis

Data was initially entered into an EXCEL spread sheet and then into IBM Statistical Package for Social Sciences (SPSS) program. Statistical analysis presents descriptive statistics of demographic variables, t-test comparison of the sample response to national mean scores of the Health Related Quality of Life (HRQoL SF12, ANOVA and t-tests that examine the relationship of demographic variables to the HRQoL and the Herth Hope Index (HHI).

Correlational analyses that assessed the relationship between HHI and the domains of the

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HRQoL SF12 and the medical illnesses of the sample. Regression analysis assessed the combination of domains of the HRQoL SF12 best predicted scores on the HHI. Statistical significance was indicated by level of significance p = <.05.

Chapter Summary

Research methodology was presented in Chapter 3. The sample population and inclusion and exclusion criteria were detailed. Protection of the participants via informed consent and IRB review and approval were discussed. The tools that were used to measure hope and health related quality of life, the HHI and SF 12 were described and the reliability and validity of both tools were presented. Data collection and analysis were outlined.

The results of the study including statistical analysis of demographics, relation between demographic variables to HRQoL health domains and hope along with reliability of the survey tools are covered in Chapter 4.

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Chapter 4 Results

This chapter presents the summary the pilot study and summary of the statistical results of this study in narrative form and depicted in table format. Statistical analysis presents

descriptive findings of demographic variables in Tables 1-5. Table 6 presents t-test comparison of the sample response to national mean scores of the Health Related Quality of Life (HRQoL SF12). ANOVA and t-tests that examine the relationship of demographic variables to the HRQoL and the Herth Hope Index (HHI) are presented in Tables 7-10. Correlational analyses that assessed the relationship between HHI and the domains of the HRQoL SF12 and the medical illnesses of the sample is presented in Table 11. Table 12 presents the intercorrelation between the domains of HRQoL SF12. Regression analysis that assessed the combination of domains of the HRQoL SF12 best predicted scores on the HHI is presented in Table 13. Finally, additional analyses is discussed.

Pilot Study

Prior to the full scale study, a pilot study was performed involving a cluster sample of volunteer participants (N=10) who met all parameters for inclusion. The purpose of the pilot study was to test the tools and to assure the sampling frame and technique were effective.

Each woman in the pilot sample completed the Demographic questionnaire, HRQoL SF12 and Herth Hope Index. Descriptive data revealed that 80% were age 75 years and older;

100% of the pilot sample had one or more concurrent medical illness and 80% had hypertension;

9 were Non-Hispanic White and 1 was Black/African American; 100% were single; 90%

graduated from high school and beyond.

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Pilot data was analyzed using Non-Parametric Spearman correlation for comparison of the HRQoL SF12 health domains and Herth Hope Index (p> 0.05). Although no statistically significant correlation was noted, the demographic data exposed trends in age, concurrent medical illnesses and marital status that were worth probing. Cronbach’s alpha were calculated on both tools, with HHI (α=.817); SF12 (α=.896). Review of pilot study lead to the conclusion that the sampling frame and technique was effective, hence the study was carried out as planned.

Descriptive Findings

The study sample amount (N=91) satisfied the G*power analysis minimum requirement (N > 84). Descriptive statistics are presented in tables 1 through 5.

Age: Approximately 68% of the sample was over age 75. The 75 to 79 age group was the largest (24.2%) and the 65 to 69 age group was the smallest (13.2%) in the sample.

Distribution of age is presented in Table 1.

Table 1

Age Distribution

____________________________________________________________________________

Age Group Frequency (f) Percent (%)

____________________________________________________________________________

65 years to 69 Years 12 13.2

70 years to 74 Years 17 18.7

75 years to 79 Years 22 24.2

80 years to 84 Years 21 23.1

85 years and older 19 20.9

Medical History: A majority (70.3%) had a history of hypertension, while 41.8% had a history of arthritis. Fewer participants reported history of diabetes (16.5%), COPD (11.0%), cancer (16.5%), stroke (2.2%) and other (12.1%). Participants had at least 1 concurrent medical illnesses

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(x̅=1.71) beyond and above heart attack with a range of one to four concurrent medical illnesses.

Distribution of concurrent medical illnesses is presented in Table 2.

Table 2

Distribution of Concurrent Medical Illnesses

_____________________________________________________________________________

Medical Illness Count Percent (%)

Hypertension 64 70.3

Diabetes of High Blood Sugar 15 16.5

COPD 10 11

Cancer 15 16.5

Stroke 2 2.2

Arthritis 38 41.8

Other 11 12.1

____________________________________________________________________________

Ethnicity: A majority of the participants identified as Non-Hispanic White (75.8%), followed by Black or African American (16.5%), Native American or Indian (4.4%), Hispanic or Latino (2.2%) and other (1.1%). Distribution of ethnicity is presented in Table 3.

Table 3

Distribution of Ethnicity

____________________________________________________________________________

Ethnic Group Count Percent

____________________________________________________________________________

Asian/Pacific Islander 1 1.1

Black or African American

1 15

1.1 16.5

Hispanic or Latino 2 2.2

Native American/American Indian 4 4.4

Non-Hispanic White 69 75.8

____________________________________________________________________________

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Marital Status: Approximately half of the participants were widowed (49.5%), while 23.1%

were married. The divorced/separated group comprised 17.6% of the sample, followed by the single/never married group (7.7%) and the unmarried in a relationship group (2.2%). The married and unmarried in a relationship groups were combined, referred to as the married group and compared to the combination of the single/never married, widowed and divorced/separated groups. The marital status of the participants is presented in table 4.

Table 4

Distribution of Marital Status

________________________________________________________________________

Marital Status Count Percent

____________________________________________________________________________

Single / Never Married 7 7.7

Married 21 23.1

Widowed 45 49.5

Divorced / Separated 16 17.6

Member of Unmarried Relationship 2 2.1

______________________________________________________________________________

Highest Educational Level: The greatest portion of the sample listed high school graduate as their highest level of education (41.8%). This was followed by college graduate (26.4%), some college (20.9%), some high school (7.7%) and completed elementary (3.3%). The distribution of education level of the participants is presented in table 5.

Table 5

Distribution of Educational Levels

____________________________________________________________________________

Count Percent of Total Sample

____________________________________________________________________________

Completed Elementary 3 3.3

Some High School 7 7.7

Graduated High School 38 41.8

Some College 19 20.9

Graduated College 24 26.4

Figure

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